An 8B LLM agent with code-based spatiotemporal analytics, procedural memory, and multi-stage agentic RL coordinates six coupled traffic-control tasks across three NYC boroughs in SUMO.
Browsemaster: Towards scalable web browsing via tool-augmented programmatic agent pair
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Effective information seeking in the vast and ever-growing digital landscape requires balancing expansive search with strategic reasoning. Current large language model (LLM)-based agents struggle to achieve this balance due to limitations in search breadth and reasoning depth, where slow, serial querying restricts coverage of relevant sources and noisy raw inputs disrupt the continuity of multi-step reasoning. To address these challenges, we propose BrowseMaster, a scalable framework built around a programmatically augmented planner-executor agent pair. The planner formulates and adapts search strategies based on task constraints, while the executor conducts efficient, targeted retrieval to supply the planner with concise, relevant evidence. This division of labor preserves coherent, long-horizon reasoning while sustaining broad and systematic exploration, overcoming the trade-off that limits existing agents. Extensive experiments on challenging English and Chinese benchmarks show that BrowseMaster consistently outperforms open-source and proprietary baselines, achieving scores of 30.0 on BrowseComp-en and 46.5 on BrowseComp-zh, which demonstrates its strong capability in complex, reasoning-heavy information-seeking tasks at scale.
years
2026 3representative citing papers
EvoMaster is an open-source agent framework whose iterative self-evolution loop achieves the highest average score on 9 of 10 scientific agent benchmarks compared to OpenHands, OpenClaw, and Codex.
Retrieval-augmented generation reaches 0.66 weighted F1 for invalid bug report subclassification while agentic web search reaches 68.9% Judge LLM success for no-code fix generation.
citing papers explorer
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TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control
An 8B LLM agent with code-based spatiotemporal analytics, procedural memory, and multi-stage agentic RL coordinates six coupled traffic-control tasks across three NYC boroughs in SUMO.
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EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale
EvoMaster is an open-source agent framework whose iterative self-evolution loop achieves the highest average score on 9 of 10 scientific agent benchmarks compared to OpenHands, OpenClaw, and Codex.
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Automated Root-Cause Subclassification and No-Code Fix Generation for Invalid Bug Reports
Retrieval-augmented generation reaches 0.66 weighted F1 for invalid bug report subclassification while agentic web search reaches 68.9% Judge LLM success for no-code fix generation.